Background: cowplot

• cowplot: short for Claus O. Wilkes plot package, initially developed for Claus’s lab members

• add on to ggplot2

cowplot is described as

provid[ing] various features that help with creating publication-
quality figures, such as a set of themes, functions to align plots and
arrange them into complex compound figures, and functions that make it easy
to annotate plots and or mix plots with images.

Loading libraries

library("readr")
library("ggplot2")
library("cowplot")
library("RColorBrewer")

Set up data and plot

• read antigenic cartography data from Smith et al. (2004)

coord <- read_csv("data/2004_Science_Smith_data.csv")

## Rows: 322 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): name, cluster, type, location
## dbl (5): year, x.coordinate, y.coordinate, lat, lng
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

• Initiate ggplot2 object

p <- ggplot(data=coord)

Individual visualisations

• recreate plots from “R bootcamp - part 2: visualisation with ggplot2”
• save them in individual ggplot2 objects

antigenic_map <- p + 
  geom_point(aes(x=x.coordinate, y=y.coordinate, color=cluster)) +
  scale_color_brewer(type="qual", palette = "Set3") +
  labs(x="Dimension 1 [AU]",
       y="Dimension 2 [AU]",
       color="Cluster") +
  theme_bw()

time_barplot <- p + geom_bar(aes(x=year, fill=cluster),
                             position=position_dodge(preserve="single")) +
  scale_fill_brewer(type="qual", palette = "Set3") +
  labs(x="Count",
       y="Year",
       fill="Cluster") +
  theme_bw()

time_boxplot <- p + 
  geom_boxplot(aes(x=type, y=year, color=type)) +
  geom_jitter(aes(x=type, y=year, color=type)) +
  scale_color_manual(values=c("#66c2a5", "#fc8d62")) +
  labs(x="Type",
       y="Time",
       color="Type") +
  theme_bw()

Simple compound plots

plot_grid(time_barplot, time_boxplot)

Customising plot_grid: Labels

• specifying the desired labels in a vector
  • reminder: vectors are created with c(), by providing its elements separated by commons, e.g. c(1.6, 2.5, 3.2) is a vector with three elements of the type double.
  • labels have to be provided for all or none of the panels i.e. the length of the vector has to be the same as number of panels
  • to label only certain panels make use of the empty string: "" e.g. c("a", "") will label first panel with a, second panel with nothing
• auto-generate labels with:
  • labels="AUTO" for upper-case labels
  • labels="auto" for lower-case labels.

plot_grid(time_barplot, time_boxplot,
          labels=c(1,2))

Customising plot_grid: Layout

Specify layout with the number of colums ncol or number or rows nrow:

plot_grid(time_barplot, time_boxplot,
          labels=c(1,2),
          nrow=2)

Customising plot_grid: Alignment

• plot_grid default: axis titles alignement
• To align by actual axis: use combination of the align and axis

plot_grid(time_barplot, time_boxplot,
          labels=c(1,2),
          nrow=2,
          align="v",
          axis="lr")

Exercises: simple compound plots

1. Autogenerate lower-case labels for the compound plot of time_boxplot and time_barplot.
2. Change the default layout by specifying the number of columns.
3. Read the help function for plot_grid and experiment with other axis options.

Exercises: simple compound plots

1. Autogenerate lower-case labels for the compound plot of time_boxplot and time_barplot.

plot_grid(time_boxplot, time_barplot,
          labels="auto",
          nrow=2,
          align="v",
          axis="lr")

Exercises: simple compound plots

2. Change the default layout by specifying the number of columns.

plot_grid(time_boxplot, time_barplot,
          labels="auto",
          ncol=1,
          align="v",
          axis="lr")

Exercises: simple compound plots

3. Read the help function for plot_grid and experiment with other axis options

?plot_grid

align     (optional) Specifies whether graphs in the grid should be
          horizontally ("h") or vertically ("v") aligned. Options are "none"
          (default), "hv" (align in both directions), "h", and "v".

axis      (optional) Specifies whether graphs should be aligned by the left
          ("l"), right ("r"), top ("t"), or bottom ("b") margins. Options
          are "none" (default), or a string of any combination of l, r, t,
          and b in any order (e.g. "tblr" or "rlbt" for aligning all
          margins).

Compound plots with more than two panels

plot_grid(time_barplot, time_boxplot, antigenic_map)

This layout does not look great, for several reason:

1. We have a void in the lower right corner;
2. The heights of both rows are the same, which unnessecarily stretches the bar and boxplot.

Nested compound plots

1. Generate composite for the right-hand column with time_barplot and time_boxplot;
2. Combine with the antigenic map.

right_column <- plot_grid(time_boxplot, time_barplot,
                   nrow=2,
                   align="v",
                   axis="lr")

plot_grid(antigenic_map, right_column,
          nrow=1,
          align="h",
          axis="t")

Shared legends in compound plots

• remove the individual legends;
• shared legend as an additional row to plot_grid

Shared legends, step 1: create individual panel components

• Add legend.position via theme:

  • boxplot legend below plot with theme(legend.position = "bottom");
  • barplot and anitgentic map legend removed with theme(legend.position = "none").

Note: We only move and remove the legends from the objects that we pass to plot_grid, the original objects remain unchanged.

right_column <- plot_grid( time_boxplot + theme(legend.position = "bottom"),
                           time_barplot + theme(legend.position = "none"),
                           nrow=2,
                           align="v",
                           axis="lr")

top_row <- plot_grid(antigenic_map + theme(legend.position = "none"),
                     right_column, nrow=1)

Shared legends, step 2: extract common legend

• Create object with desired design of common legend:

  • legend at bottom of plot with theme(legend.position = "bottom");
  • legend with two row layout with guides(color=guide_legend(nrow=2)).

• Extract the legend from the new_map object with get_legend:

new_map <- antigenic_map +
  guides(color=guide_legend(nrow=2)) +
  theme(legend.position = "bottom")

cluster_legend <- get_legend(new_map)

Shared legends, step 3: combine panels and common legend

• combine components in a new call to plot_grid;
• specify rel_heights for ratio of compound panel to shared legend height.

plot_grid(top_row, cluster_legend,
          nrow=2,
          rel_heights = c(10,1))

Exercises: shared legends

1. What would be a good labeling strategy for this nested plot? Label accordingly.
2. Play with the ratios provided to rel_heights. Why does it not make sense to provide rel_widths in this example?

Exercises: shared legends

1. What would be a good labeling strategy for this nested plot? Label accordingly.

right_column <- plot_grid( time_boxplot + theme(legend.position = "bottom"),
                           time_barplot + theme(legend.position = "none"),
                           nrow=2,
                           align="v",
                           axis="lr",
                           labels=c("b", "c"))

top_row <- plot_grid(antigenic_map + theme(legend.position = "none"),
                     right_column, nrow=1,
                     labels=c("a", ""))

plot_grid(top_row, cluster_legend,
          nrow=2,
          rel_heights = c(10,1))

Exercises: shared legends

2. Play with the ratios provided to rel_heights. Why does it not make sense to provide rel_widths in this example?

plot_grid(top_row, cluster_legend,
          nrow=2,
          rel_heights = c(4,1))

rel_widths only useful when putting panels in multiple columns, not multiple rows.

cowplot themes

plot_grid(antigenic_map + theme_gray() + ggtitle("Default ggplot"),
          antigenic_map + theme_cowplot()  + ggtitle("theme_cowplot"),
          antigenic_map + theme_half_open()  + ggtitle("theme_half_open"), 
          antigenic_map + theme_minimal_grid() + ggtitle("theme_minimal_grid"),
          antigenic_map + theme_minimal_hgrid() + ggtitle("theme_minimal_hgrid"),
          antigenic_map + theme_minimal_vgrid() + ggtitle("theme_minimal_vgrid"),
          antigenic_map + theme_map() + ggtitle("theme_map"),
          antigenic_map + theme_nothing() + ggtitle("theme_nothing"),
          nrow=4,
          align="vh",
          axis="tblr")

The lower right plot shows theme_nothing by cowplot.

Note: Not all themes are equally well suited for each plot type. The ggplot2 default for boxplots chooses a simply horizontal grid for instance, where as the same theme applied to the scatter plot display horizontal and vertical grids.

Exercises: cowplot themes

1. Discuss which other plot type might benefit from a simpler grid than the default one chosen?
2. What happens if you add a theme to plot_grid?
3. Apply different themes to the three individual plots and visualise in plot_grid.

Exercises: cowplot themes

1. Discuss which other plot type might benefit from a simpler grid than the default one chosen?

Barplot with horizontal lines, similar as in boxplot

Exercises: cowplot themes

2. What happens if you add a theme to plot_grid?

plot_grid(top_row, cluster_legend,
          nrow=2,
          rel_heights = c(10,1)) +
  theme_cowplot()

Exercises: cowplot themes

3. Apply different themes to the three individual plots and visualise in plot_grid.

boxplot_new <- time_boxplot +
  theme_cowplot() +
  theme(legend.position = "bottom")

barplot_new <- time_barplot + 
  theme_minimal_hgrid() +
  theme(legend.position = "none")

map_new <- antigenic_map + 
  theme_half_open() +
  theme(legend.position = "none")

right_column <- plot_grid(boxplot_new,
                          barplot_new,
                          nrow=2,
                          align="v",
                          axis="lr",
                          labels=c("b", "c"))

top_row <- plot_grid(map_new,
                     right_column, nrow=1,
                     labels=c("a", ""))

plot_grid(top_row, cluster_legend,
          nrow=2,
          rel_heights = c(10,1))

Additional material

The ggpubr package provides some easy-to-use functions for creating and customizing ggplot2- based publication ready plots: ggplot2 Based Publication Ready Plots

References

Cowplot documentation on this webpage: cowplot